Planning with tensor networks based on active inference

Autor(en)

Samuel T. Wauthier, Tim Verbelen, Bart Dhoedt, Bram Vanhecke

Abstrakt

Tensor networks (TNs) have seen an increase in applications in recent years. While they were originally developed to model many-body quantum systems, their usage has expanded into the field of machine learning. This work adds to the growing range of applications by focusing on planning by combining the generative modeling capabilities of matrix product states and the action selection algorithm provided by active inference. Their ability to deal with the curse of dimensionality, to represent probability distributions, and to dynamically discover hidden variables make matrix product states specifically an interesting choice to use as the generative model in active inference, which relies on ‘beliefs’ about hidden states within an environment. We evaluate our method on the T-maze and Frozen Lake environments, and show that the TN-based agent acts Bayes optimally as expected under active inference.

Organisation(en)

Quantenoptik, Quantennanophysik und Quanteninformation

Externe Organisation(en)

Ghent University , VERSES AI Research Lab

Journal

Machine Learning: Science and Technology

Band

5

Anzahl der Seiten

22

ISSN

2632-2153

DOI

https://doi.org/10.1088/2632-2153/ad7571

Publikationsdatum

10-2024

Peer-reviewed

Ja

ÖFOS 2012

103025 Quantenmechanik, 102019 Machine Learning

Schlagwörter

ASJC Scopus Sachgebiete

Software, Artificial Intelligence, Human-computer interaction

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/04549a27-d440-4055-8ce4-35a98516f612